This repository includes the evaluation code for the paper "Regularizing Proxies with Multi-Adversarial Training for Unsupervised Domain-Adaptive Semantic Segmentation (Arxiv Link)". The whole package will be made public soom.
mxnet
gluoncv
numpy
tqdm
easydict
yaml
pillow
To evaluate the performance on Cityscapes dataset, please first put the dataset into the correct path. Please edit the variable "data_root" in "cfg/resnet101_gta2cs.yaml" and "cfg/resnet101_syn2cs.yaml", which points to the data root. Then name the Cityscapes folder "cityscapes" and put it in the data root.
We provide two resnet101 models for GTAV->CS and SYNTHIA->CS respectively. first download the models HERE and HERE:
To evaluate them, simply run:
python eval.py --cfg cfg/resnet101_gta2cs.yaml --resume resnet101_gta2cs.params
python eval.py --cfg cfg/resnet101_syn2cs.yaml --resume resnet101_syn2cs.params